Olostep provides scalable web data APIs, specifically designed for the web’s second user - AI. Effortlessly turn any URL into LLM-ready Markdown, JSON, or structured data.
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Hey Product Hunt 👋
AI agents are becoming the Web’s second user.
But most agents still access the web like humans do: open tabs, wait for pages, fight JavaScript, parse messy HTML, maintain brittle scrapers, retry failed jobs, and hope the data is fresh.
That felt backwards.
So @hamza_ali59 built Olostep: web infrastructure for AI agents - one API to search, scrape, crawl, map, monitor, and structure live web data for AI products and automation workflows.
What you can do with Olostep:
🔎 Search the live web with natural language queries
🧹 Scrape any URL into clean Markdown, HTML, JSON, screenshots, PDFs, or raw content
🕸️ Crawl entire websites and retrieve LLM-ready pages
🗺️ Map a domain and discover URLs beyond basic sitemaps
⚡ Batch process large URL lists for production data pipelines
📡 Monitor pages, prices, job openings, DOM changes, content updates, and business signals
🤖 Answer questions with web-grounded sources and structured output
🧩 Connect it to your stack with SDKs, CLI, MCP, n8n, LangChain, Mastra, Apify, Zapier, Cursor, Claude, and more
Why Hamza built this:
The bottleneck for AI products is often not the model anymore.
It’s the web layer.
Agents need fresh, structured, reliable data from the real world. Not stale training data. Not one-off browser scripts. Not scrapers that break every time a website changes.
We believe the next generation of AI companies will need a production-grade way for agents to read, reason over, and act on the web.
That is what Olostep is becoming.
Who it is for:
AI teams building agents, copilots, and RAG pipelines
Developers who need clean web data without maintaining scraping infrastructure
Growth, sales, and data teams enriching companies, leads, spreadsheets, and CRMs
SEO / GEO teams monitoring brand visibility across search and AI answers
Recruiting, finance, research, ecommerce, and competitive intelligence teams automating web research
Try this today:
Give Olostep one messy web workflow you currently do manually - for example:
“monitor 50 competitor pricing pages every week”
“crawl these docs and turn them into clean RAG context”
“extract structured product data from a marketplace”
“find companies hiring for a specific role and enrich them”
…and Olostep should help turn that workflow into clean, structured data your app or agent can actually use.
🎁 Product Hunt launch offer: They are giving PH builders 80% off for the first month, on any plan! Use code PH80 or comment here after signing up and we’ll help you get set up.
Excited to hear what you think :)
Report
How do you decide when parsers should handle extraction versus letting the model interpret the data?
Report
Does it also preserve enough page context for AI agents to understand relationships between different pieces of web data?
Replies
Hey Product Hunt 👋
AI agents are becoming the Web’s second user.
But most agents still access the web like humans do: open tabs, wait for pages, fight JavaScript, parse messy HTML, maintain brittle scrapers, retry failed jobs, and hope the data is fresh.
That felt backwards.
So @hamza_ali59 built Olostep: web infrastructure for AI agents - one API to search, scrape, crawl, map, monitor, and structure live web data for AI products and automation workflows.
What you can do with Olostep:
🔎 Search the live web with natural language queries
🧹 Scrape any URL into clean Markdown, HTML, JSON, screenshots, PDFs, or raw content
🕸️ Crawl entire websites and retrieve LLM-ready pages
🗺️ Map a domain and discover URLs beyond basic sitemaps
⚡ Batch process large URL lists for production data pipelines
📡 Monitor pages, prices, job openings, DOM changes, content updates, and business signals
🤖 Answer questions with web-grounded sources and structured output
🧩 Connect it to your stack with SDKs, CLI, MCP, n8n, LangChain, Mastra, Apify, Zapier, Cursor, Claude, and more
Why Hamza built this:
The bottleneck for AI products is often not the model anymore.
It’s the web layer.
Agents need fresh, structured, reliable data from the real world. Not stale training data. Not one-off browser scripts. Not scrapers that break every time a website changes.
We believe the next generation of AI companies will need a production-grade way for agents to read, reason over, and act on the web.
That is what Olostep is becoming.
Who it is for:
AI teams building agents, copilots, and RAG pipelines
Developers who need clean web data without maintaining scraping infrastructure
Growth, sales, and data teams enriching companies, leads, spreadsheets, and CRMs
SEO / GEO teams monitoring brand visibility across search and AI answers
Recruiting, finance, research, ecommerce, and competitive intelligence teams automating web research
Try this today:
Give Olostep one messy web workflow you currently do manually - for example:
“monitor 50 competitor pricing pages every week”
“crawl these docs and turn them into clean RAG context”
“extract structured product data from a marketplace”
“find companies hiring for a specific role and enrich them”
…and Olostep should help turn that workflow into clean, structured data your app or agent can actually use.
🧪 Try it: Get a free API key
📚 Docs: Read the docs
💬 Community: Join our Slack
🎁 Product Hunt launch offer: They are giving PH builders 80% off for the first month, on any plan! Use code PH80 or comment here after signing up and we’ll help you get set up.
Excited to hear what you think :)
How do you decide when parsers should handle extraction versus letting the model interpret the data?
Does it also preserve enough page context for AI agents to understand relationships between different pieces of web data?